Selective maintenance has a significant impact on the sustainable management of maintenance operations. The collaboration of multiple maintenance teams/operators is helpful to achieve sustainability for selective maintenance sequence planning. For products with a large number of components, a single maintenance team/operator is inefficient due to a long completion time which is not acceptable for emergency planning. Providing specific and efficient maintenance sequence planning is critical to effectively handle different types of emergencies (e.g., wartime) while avoiding vague task assignments to multiple maintenance teams/operators. For scheduling many maintenance jobs while improving the efficiency and quality of maintenance operations, this study proposes a collaborative maintenance planning based on the concept of imperfect maintenance. In this regard, this study develops a multi-objective optimization model to optimize parallel maintenance sequences considering maintenance profit, maintenance cost, maintenance team, and resource limitations. We show the feasibility of the proposed multi-objective optimization model through a real case of maintenance practice for the components of an assistor device. For analyzing the complexity of the proposed maintenance sequence planning problem, this study introduces a new multi-objective metaheuristic algorithm which is an enhanced multi-objective gravitational search algorithm (EMOGSA) to find high-quality Pareto solutions for the proposed problem. Different multi-objective evaluation metrics are used to study the performance of the proposed algorithm. From the results, the proposed model and developed solution algorithm can help maintenance decision-makers to determine complex maintenance planning.Note to Practitioners—This paper deals product with a maintenance and proposes gravitational search algorithm based on only maintenance task, which maintenance task. The goal of this paper is to analyze the maintenance problem from the perspective of collaboration of multiple maintenance teams/operators.

Design for disassembly (DFD) is a basic design technology serving the scrap and recycling stages, which can relieve the environmental pressure and improve the economic benefits. Considering the wide variety of mechanical products and the shortening of product lifecycle, the product design integrating customer requirements can effectively improve the market competitiveness of products. Thus, this paper proposes a QFD-based human-centric decision-making approach for disassembly scheme selection. The theory of the interval 2-tuple q-rung orthopair fuzzy sets (I2q-ROFSs) is proposed to better describe the ambiguity of the environment and avoid information loss/distortion in the information aggregation stage. A scheme evaluation system based on the disassembly technical features is established. A hybrid multi-attribute decision-making (MADM) method combing BWM-FQFD (best worst method and fuzzy quality function deployment) and RT-EDAS (the regret theory based on distance from average solution) is presented to obtain the optimal alternative. A case study, i.e., four refrigerator DFD schemes, is applied to verify the effectiveness of the proposed method. The result demonstrates that this work provides an effective tool to select the optimal scheme of disassembly considering customer requirements and some references for designersNote to Practitioners—The selection of DFD schemes is the last task of the disassembly design. The decision information of the disassembly design scheme has insufficient evaluation complexity ability and the information distortion of the evaluation information in the aggregation stage. To end this, this paper proposes I2q-ROFSs which is a fuzzy set with the higher level of fuzzy description and avoiding information distortion. At present, the disassembly design research lacks the correlation between customer demand and disassembly technology features. Therefore, a fuzzy QFD method can well assist manufacturing enterprises to improve the market competitiveness of their products. Then, RT-EDAS can describe consumer psychology, which can choose a more market-appropriate DFD scheme.

As a valuable tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) have obtained widespread application in many fields and achieved ideal results to some degree. However, current methods ignore the reliability of expert evaluations and the indirect effects among propositions, which may lead to decision-making errors or inaccurate evaluations. Thus, the indirect-effect-incorporated linguistic Z-number Petri nets (ILZPNs) are proposed in this paper. The linguistic Z-number is presented to capture knowledge information more comprehensively with its related fuzzy rules. The concepts of indirect effects and its aggression operators are designed to enhance the knowledge reasoning capability of ILZPN. Besides, the formal definition and the corresponding simulation algorithm of ILZPN are presented to conduct knowledge representation and reasoning. Subsequently, an empirical case, i.e., generalized eco-driving behavior evaluation, is applied to verify the proposed approach. In addition, comparison and sensitivity analysis are performed to monitor the robustness of the results. The results prove that this study offers a significant reference for the research into similar issues.

To cope with the problems of high-computational complexity and multiple locally optimal solutions induced by the coupling of multiple subproblems, conflicting objectives, and integration of resource constraints of the energy-efficient lot-streaming scheduling of multi-resource constrained flexible job shop (γ-shop for short), an energy-efficient lot-streaming scheduling optimization approach based on the knowledge-based lot-splitting method (KLSM) and the improved multiobjective evolutionary algorithm (IMOEA) is presented. First, a flexible job shop lot-splitting scheduling model with the optimization objectives of total energy consumption, makespan, and total processing cost is formulated. Second, a hybrid approach of the KLSM and the IMOEA is designed to solve the model The solution space of the problem is fully explored based on the moth-flame operator. Co-evolutionary operators are performed to promote information interaction among populations, hence both the population diversity and the convergence effect of the algorithm are improved. Moreover, a post-adjustment strategy based on adjacent processes is developed to reduce unnecessary fixture changes. Finally, extended experiments between some KLSM-based well-known and novel algorithms, including the proposed IMOEA, MOEA/D, NSGA-II, MOPSO, SGECF, SCEA, and SLMEA are conducted in benchmark problems and a real-world case of machine tool plant. The results show that the proposed method outperforms its competitors on co-optimization of lot-splitting, machine allocation, operation sequencing, and fixture assignment of the γ-shop scheduling, which can effectively reduce total energy consumption, makespan, and total processing cost.

The development of lot streaming technology in flexible production systems has greatly benefited the aerospace, semiconductor, automotive, pharmaceutical and other discrete manufacturing industries. In this paper, we address a lot-splitting optimization problem of flexible job shop. To handle the issues of large performance gap and low searching efficiency of optimal scheme caused by strong randomness of lot-splitting, a knowledge-based method is proposed. First, under the equal lot-splitting strategy and variable minimum sub-lots constraint, a lot-splitting scheduling model of flexible job shop is constructed. Second, the effectiveness of lot-splitting schemes is assessed by appropriate data-driven algorithms according to the different scales of the problem. Moreover, a neighborhood search strategy based on the number of sub-lots is introduced to improve the search ability of the method. Then, by analyzing the characteristics of the problem, a parameter adjustment strategy based on problem-specific knowledge is presented to balance the search time and performance. Next, sensitivity analyses are carried out to calibrate the parameters based on the design of experiment Taguchi method. Finally, extensive experiments are conducted to evaluate the effectiveness of the proposed method based on the benchmark problems with single or multiple objectives. The results show that the proposed method can obtain high-quality optimized lot-splitting schemes.Note to Practitioners—In discrete manufacturing industries, the flexible production mode in multi-variety and small-batch has become the mainstream. The flexible job shop lot-splitting optimization problem in such a mode is of great significance to practitioners. We mathematically characterize the lot-splitting problem in a flexible job shop considering energy consumption and solve it through a new general multi-objective optimization framework by integrating the lot-splitting and job shop scheduling that is activated from problem-specific knowledge. Existing lot-splitting optimization approaches fail to solve the concerned problem. This work introduces appropriate data-driven algorithms to assess the scheduling schemes after lot-splitting. By updating the minimum lot-size of jobs and the archive set, the search of the optimal lot-splitting scheme is directional. Furthermore, the global and local iterations of the method are adjusted according to the problem size and the number of sub-lots to balance the performance and search time. The results of comparative study show that the proposed method outperforms other algorithms well and can greatly help practitioners manage the lot-splitting scheme of jobs.

The multiple signal classification (MUSIC) algorithm based on the orthogonality between the signal subspace and noise subspace is one of the most frequently used methods in the estimation of direction of arrival (DOA), and its performance of DOA estimation mainly depends on the accuracy of the noise subspace. In the most existing researches, the noise subspace is formed by (defined as) the eigenvectors corresponding to all small eigenvalues of the array output covariance matrix. However, we found that the estimation of DOA through the noise subspace in the traditional formation is not optimal in almost all cases, and using a partial noise subspace can always obtain optimal estimation results. In other words, the subspace spanned by the eigenvectors corresponding to a part of the small eigenvalues is more representative of the noise subspace. We demonstrate this conclusion through a number of experiments. Thus, it seems that which and how many eigenvectors should be selected to form the partial noise subspace would be an interesting issue. In addition, this research poses a much general problem: how to select eigenvectors to determine an optimal noise subspace?

A disassembly line is an industrialized and automated production line which should be scheduled with high production efficiency. Although many disassembly line balancing optimization studies are contributed recently, they increase or reduce the number of workstations to balance the disassembly line. From real-world managerial settings, an increase or decrease workstations, is too expensive and not realistic. The bucket brigades’ disassembly line is self-balancing and self-organizing, which is not constrained by the workstation beat time and only needs to distribute workers on the line according to certain rules to achieve line balancing after a period of time. In this article, a bucket brigades disassembly line balancing optimization method considering uncertainty is proposed, in which a cloud model is used to represent the uncertain disassembly time. The proposed model handles multiple objectives including smoothness, disassembly cost and disassembly energy consumption to be minimized. To solve this complex problem, this article innovates a new heuristic method based on the social engineering optimizer as an enhanced local search metaheuristic. Finally, a ball collector is used to verify the effectiveness of the proposed method and extensive analysis is done to compare the performance of proposed model with other recent algorithms.

This article proposes a risk assessment method based on interval intuitionistic integrated cloud Petri net (IIICPN). The cloud model is widely used in data mining and knowledge discovery, especially in risk assessment problems with linguistic variables. However, the cloud models proposed in the literature do not express interval-valued intuitionistic linguistic satisfactorily, and the reasoning methods based on the cloud models cannot perform risk assessment well. The work in this article includes the definition of IIIC and IIICPN, the method of converting the interval-valued intuitionistic uncertain linguistic numbers into IIIC, and the reasoning method of IIICPN. As proofs, a subway fire accident model is adopted to confirm the feasibility of the proposed method, and comparison experiments between the IIICPN with general fuzzy Petri net and the trapezium cloud model are conducted to verify the superiority of the proposed model.Note to Practitioners—This work deals with the subway fire risk assessment problem. It proposes a cloud model based on interval-valued intuitionistic uncertain linguistic and builds a cloud-based Petri net model. The methods of fire risk assessment use the existing fault trees or aggregation operators to combine all the factors into consideration, but they do not take the interaction of factors. The goal of this work is to assess the risk of subway fire accident of subway, using fuzzy linguistic decision variables. The simulation results indicate that the proposed method is highly effective. The obtained results can help assessors better determine which factors may cause the disaster.

Multicriteria decision making (MCDM) problems are often encountered in complex system design. Most of them need to be evaluated with a large number of interactive and qualitative indices, which are difficult to be addressed effectively through the existing methods. In this paper, a novel fuzzy Choquet integral-based grey comprehensive evaluation (GCE) method, called fuzzy grey Choquet integral (FGCI), is proposed to evaluate MCDM problems with many interactive and qualitative indices. In this method, expert evaluation of qualitative indices is represented through fuzzy linguistic values. Fuzzy values are defuzzified and standardized to obtain the original evaluation matrix. The original values are replaced by the correlation coefficients, which, to a certain extent, eliminate the influence of experts' subjective preference. An improved teaching-learning-based optimization algorithm is employed to identify λ-fuzzy-measures following the weights given by experts in order to enhance the consistency of weights. Then the correlation coefficients are aggregated through Choquet integral among λ-fuzzy-measures, which can reflect interactions among indices. In addition, according to the characteristics of λ-fuzzy-measures, the construction guidelines for a corresponding index system are given to overcome the limitations of FGCI. Finally, the performance of the proposed method is demonstrated via a practical example of green design evaluation and compared with the GCE method. The results validate its feasibility and effectiveness.

With the expansion of the sharing economy, growing urban traffic, and increasing environmental pollution, bike-sharing systems (BSSs) are developing rapidly all over the world. A major operational issue in BSS is to reposition the bikes over time such that enough bikes and open parking slots are available to users. Especially during peak hours, it is essential to stabilize BSS in use. To cope with the issue, this article proposes a new approach integrating multiobjective optimization and a weighting factor based on the shortage event types of each station. In addition, the multiobjective artificial bee colony algorithm is modified according to the features of this work to find optimal solutions. The proposed approach is applied to the real-life repositioning of a BSS during peak hours to verify its feasibility and effectiveness. Also, the algorithm is compared with other frequently used multiobjective algorithms. For the comparative study, convergence metric and spacing are adopted to further measure the algorithm performance. The scalability of the proposed approach in addressing the multiobjective repositioning problems during peak hours is also verified by multiple trials. Note to Practitioners-This work deals with bike repositioning in bike-sharing systems (BSSs) during peak hours, which has major significance in the efficient operation of such systems. It builds a multiobjective optimization model and solves it through a modified multiobjective artificial bee colony algorithm. The existing single-objective optimization methods fail to solve the concerned problem. This work can find the optimal routes of the repositioning vehicles along with the number of desired parked bikes of corresponding stations. The experimental results indicate that the proposed method is highly effective and can greatly and readily help decision-makers better manage the BSS of a practical size.

With the increasing speed of railway vehicles, deciding how to reasonably distribute impact energy to each vehicle has been a widespread concern in safety protection systems. This article formulates a three-dimensional train-track coupling dynamics model using MAthematical DYnamic MOdels (MADYMO) multibody dynamics software. A train-to-train collision is then simulated using this model. A hybrid solution methodology that combines the non-dominated sorting genetic algorithm II (NSGA-II), modified best and worst method with cloud model theory and grey relational analysis is proposed. The optimization parameters and objectives are determined based on the EN15227 crashworthiness requirements for railway vehicles. An empirical case of an existing train with eight vehicles that have been in operation in China is applied to verify this dynamics model derived from a high-speed train and solution methodology. Analysis and discussion are conducted to monitor the robustness of the results and the practical implications for rail transportation are summarized. The results prove that the obtained optimal solution by this research has better crashworthiness than an existing solution.

With growing environmental and sustainability-related concerns, recovery optimization of mechanical products has been gaining increased exposure. It facilitates environmental sustainability through the improvement in the life-cycle material efficiency and reduction in environmental impact with disassembly sequence planning, component reuse, and material recycling. Traditional product recovery separates end-of-life (EOL) products into components and selects EOL options of components. However, there are many practical cases in which the recovery of a set of subassemblies and components leads to better net revenue than that of a complete set of single components. This paper proposes to model and optimize hybrid disassembly and EOL operations of product recovery to maximize the recovery profit and minimize the environmental impact. Flexible process planning of hybrid disassembly determines a disassembly level by identifying the reusability of subassemblies and disassembly sequences mixed with subassemblies and components. Optimal EOL decisions for each subassembly and component are investigated such that the economic and environmental objectives can be achieved. Finally, a case study is described to illustrate the proposed method and the influence on decision variables of the tradeoff between the recovery profit and environmental impact is discussed. Note to Practitioners-This paper deals with the process planning and EOL decision-making problem of product recovery. Based on hybrid disassembly, this paper proposes a flexible process planning and EOL decision-making method for product recovery. Flexible process planning of hybrid disassembly determines a disassembly level by identifying the reusability of subassemblies and disassembly sequences mixed with subassemblies and components. Optimal EOL decisions for each subassembly and component are investigated such that the economic and environmental objectives can be achieved. The goal of this paper is to model and optimize hybrid disassembly and EOL operations of product recovery to maximize the recovery profit and minimize the environmental impact. The results demonstrate that the proposed method can leads to small environmental impact and low cost. Subassemblies and components with high reliability and expensive price are suggested to be destined for reuse. Minimizing transportation distances is more effective to reduce product recovery cost. Such results can help decision makers to perform better judgments when a disassembly process of an EOL product is executed.

Disassembly sequencing is important for remanufacturing and recycling used or discarded products. AND/OR graphs (AOGs) have been applied to describe practical disassembly problems by using “AND” and “OR” nodes. An AOG-based disassembly sequence planning problem is an NP-hard combinatorial optimization problem. Heuristic evolution methods can be adopted to handle it. While precedence and “AND” relationship issues can be addressed, OR (exclusive OR) relations are not well addressed by the existing heuristic methods. Thus, an ineffective result may be obtained in practice. A conflict matrix is introduced to cope with the exclusive OR relation in an AOG graph. By using it together with precedence and succession matrices in the existing work, this work proposes an effective triple-phase adjustment method to produce feasible disassembly sequences based on an AOG graph. Energy consumption is adopted to evaluate the disassembly efficiency. Its use with the traditional economical criterion leads to a novel dual-objective optimization model such that disassembly profit is maximized and disassembly energy consumption is minimized. An improved artificial bee colony algorithm is developed to effectively generate a set of Pareto solutions for this dual-objective disassembly optimization problem. This methodology is employed to practical disassembly processes of two products to verify its feasibility and effectiveness. The results show that it is capable of rapidly generating satisfactory Pareto results and outperforms a well-known genetic algorithm.

Electric vehicles (EVs) are recognized as one of the most promising technologies worldwide to address the fossil fuel energy resource crisis and environmental pollution. As the initial work of EV charging station (EVCS) construction, site selection plays a vital role in its whole life cycle, which, however, is a complicated multiple criteria decision making (MCDM) problem involving many conflicting criteria. Therefore, this work aims to propose a novel integrated MCDM approach by a grey decision making trial and evaluation laboratory (DEMATEL) and uncertain linguistic multi-objective optimization by ratio analysis plus full multiplicative form (UL-MULTIMOORA) for determining the most suitable EVCS site in terms of multiple interrelated criteria. Specifically, the grey DEMATEL method is used to determine criteria weights and the UL-MULTIMOORA model is employed to evaluate and select the optimal site. Finally, an empirical example in Shanghai, China, is presented to demonstrate the applicability and effectiveness of the proposed approach. The results show that the proposed approach is a useful, practical, and effective way to find the optimal location of EVCSs.

Disassembly planning aims to perform the optimal disassembly sequence given a used or obsolete product in terms of cost and environmental impact. This paper presents a new multiobjective programming model for the target disassembly sequencing. It proposes an improved multiobjective ant colony algorithm to derive optimal target disassembly sequences. This work also establishes some indices on disassembly scheme evaluation and a fuzzy integral method to evaluate the obtained disassembly scheme. A CNC machine tool example is given to illustrate the proposed models and the effectiveness of the proposed algorithm. Both theoretical and simulation results demonstrate that the proposed approach can perform the quantitative analysis of a disassembly process effectively. Such results can help decision makers select the best plans and sequences when executing a disassembly process of a product.

In the highly competitive environment, capturing and disseminating of tacit knowledge are significant to an organization’s success with the development of knowledge-based systems. However, in practical knowledge acquisition process, domain experts tend to express their judgments using multigranularity linguistic term sets, and there usually exists uncertain and incomplete information since expert knowledge is experience-based and tacit. In addition, although the technical capabilities of expert systems based on fuzzy Petri nets (FPNs) are expanding, they still fall short of meeting the increasingly complex knowledge demands. Therefore, this paper develops a theoretical model based on linguistic interval 2-tuples and interval-valued intuitionistic FPNs (IVIFPNs) for acquiring and representing tacit knowledge to increase and sustain the competitive advantages of knowledge intensive organizations. An empirical case study in medical practice is provided to demonstrate the application and feasibility of the proposed model, and the results show that it can well capture experts’ tacit knowledge and reuse the acquired knowledge productively.

The effective dismantling of discarded products regardless being used or not is critically important to their reuse, recovery, and recycling. However, the existing product disassembly planning methods pay little or no attention to resource constraints, e.g., limited numbers of disassembly operators and tools. Thus, a resulting plan when being executed may be ineffective in practice. This paper presents a dual-objective optimization model for selective disassembly sequences by considering multiresource constraints such that disassembly profit is maximized and time is minimized. A scatter search is adopted to solve the proposed dual-objective optimization model. It embodies the generation of diverse initial solutions, global assessment of objective functions, a crossover combination operator, a local search strategy for improved solutions, and a reference set update method. To analyze the effect of different weights on its performance, simulations are conducted on different products. Its effectiveness is verified by comparing its optimization results and those of genetic local search.

Disassembly planning aims to search the best disassembly sequences of a given obsolete/used product in terms of economic and environmental performances. A practical disassembly process may face great uncertainty owing to various unpredictable factors. To handle it, researchers have addressed the stochastic cost and time problems of product disassembly. In reality, the uncertain environment of product disassembly is associated with both randomness and fuzziness. Besides uncertain disassembly cost and time, the quality of disassembled components/parts in a process has uncertainty and thus needs to be assessed via expert opinions/subjects. To do so, this paper presents a new AND/OR-graph-based disassembly sequence planning problem by considering uncertain component quality and varying disassembly operational cost. Important disassembly planning models are built on the basis of different disassembly criteria. A novel hybrid intelligent algorithm integrating fuzzy simulation and artificial bee colony is proposed to solve them. Its effectiveness is well illustrated through several numerical cases and comparison with a prior method, i.e., fuzzy-simulation-based genetic algorithm.

Tchebycheff decomposition represents one of the most widely used decomposition approaches that can convert a multiobjective optimization problem into a set of scalar optimization subproblems. Nevertheless, the geometric properties of the subproblem objective functions in Tchebycheff decomposition have not been explicitly studied. This paper proposes a Tchebycheff decomposition with lp-norm constraint on direction vectors in which the subproblem objective functions are endowed with clear geometric property. Especially, the Tchebycheff decomposition with l2-norm constraint on direction vectors is taken as an example to illustrate its advantage. A new unary R2indicator is also introduced to approximate the hyper-volume metric and justify the efficiency of the proposed Tchebycheff decomposition. A resultant Tchebycheff decomposition-based multiobjective evolutionary algorithm (MOEA) with l2-norm constraint and a new population update strategy is proposed to solve multiobjective optimization problems. The experimental results on both benchmark and real-world multiobjective optimization problems show that the proposed algorithm is capable of obtaining high quality solutions compared with other state-of-the-art MOEAs.

A mixed-model two-sided assembly line is a manufacturing system designed for the production of large-sized products. In order to describe the actual condition, this paper presents a novel multiobjective programming model for balancing a mixed-model two-sided assembly line subject to multiple constraints, in which, additional constraints including zoning, synchronous, and positional constraints are considered besides the traditional constraints, e.g., the precedence constraint. Two objectives are simultaneously to be optimized, one is to minimize the combination of the weighted line efficiency and the weighted smoothness index, and the other is to minimize the weighted total relevant costs per unit of a product. A novel multiobjective hybrid imperialist competitive algorithm (MOHICA) is proposed to solve this problem. In the presented MOHICA, the sigma method is employed to quantify every individual, a novel merging method is introduced to reserve better individuals into the evolutionary population, and late acceptance hill-climbing (LAHC) algorithm is presented as a local search algorithm to achieve accurate balance between intensification and diversification. The experimental results on the selected benchmark instances and a practical case show that the proposed multiobjective algorithm outperforms nondominated sorting genetic algorithm (NSGA)-II, multiobjective improved teaching-learning-based optimization, and NSGA-III existing in the literature.

The green design of electromechanical products is a pivotal link to manufacturing industry. The question on how to design green products must be answered by excellent designers using both advanced design methods and effective assessment techniques of design alternatives. Making an objective and precise assessment of green designs is of increasing importance to ensure sustainable development. This work proposes a framework based on the combination of analytical hierarchy process (AHP), gray correlation (GC), and technique for order performance by similarity to ideal solution (TOPSIS) to evaluate the performance of design alternatives. AHP is used to determine the weights of performance indices and a nonlinear programming model with constraints is proposed to obtain the integrated closeness index based on the similarity closeness index from GC and distance closeness index from TOPSIS. A case study, i.e., three kinds of refrigerators, is illustrated to verify the proposed method. By comparing with existing methods, i.e., AHP-TOPSIS and AHP-GC, the effectiveness of the proposed method has been confirmed. In addition, sensitivity analysis is also provided in order to assess the robustness of the proposed method. Also, the following implication can be obtained from our results: 1) chloro- fluoro-carbons (mg/m3) (C1), production noise (dB) (C6), and environmental cost (C16) have a large impact on refrigerator's green design since these factors carry relatively larger weights. Results of the sensitivity analysis from different cases demonstrate that the best alternative may change when different weights are assigned to the evaluation criteria. This finding means the importance of establishing a qualified group of experts/designers in design evaluation and 2) the selection of design alternatives to produce a green product is critical to product development. The main contribution of this paper is the definition and development of an effective evaluation framework to guide managers to assess product design alternatives. The results show that it overcomes the one-sidedness of AHP-TOPSIS and AHP-GC, and makes the evaluation results more objective and realistic. It provides an accurate, effective, and systematic decision support tool for green performance evaluation of product design alternatives.

A new force is introduced in the social force model (SFM) for computing following behavior in pedestrian counterflow, whereby an individual tries to approach others in the same direction to avoid conflicts with pedestrians from the opposite direction. The force, like a kind of gravitation, is modeled based on the movement state and visual field of the pedestrian, and is added to the classical SFM. The modified model is presented to study the impact of following behavior on the process of lane formation, the conflict, the number of lanes formed, and the traffic efficiency in the simulations. Simulation results show that the following behavior has a significant effect on the phenomenon of lane formation and the traffic efficiency.

With the significant improvements in the Energy Internet, we have witnessed the explosion of multisource energy big data, whose characteristics of vast volume, fast velocity, and diverse variety not only formulate an essential infrastructure of the Energy Internet, but also bring threats to the system's stability. In this paper, we concern with the system-level stability issues in the Energy Internet and study how to maintain a stable and healthy energy network environment. To this end, we propose a system-level stability evaluation model in the Energy Internet based on a critical energy function to explore small disturbance stability region (SDSR), where SDSR can be acquired via estimating the operational data threshold of distributed generations. The threshold is estimated based on energy consumption rather than equilibrium nodes, which applies the energy function theory and reduces the computation complexity. Moreover, in our proposed model, we add the big data approximate analytics algorithm into hyperplane fitting to optimize and analyze the SDSR. Simulation results on SDSR in a single dominant oscillation mode and multiple dominant oscillation mode have demonstrated the advantages and superiority of our proposed method over the prior schemes.

Fuzzy Petri net (FPN), as one type of high-level Petri nets, has attracted a lot of attention over the recent decade due to its adequacy for knowledge representation and logic reasoning. However, in the FPN literature, the truth degrees of input places are usually given directly or supposed by researchers. No or little research has been performed on the determination of initial marking vector for a specific FPN. In this correspondence paper, we introduce a group decisionmaking model using hesitant 2-tuple linguistic term sets to obtain the initial truth values of FPNs based on domain experts' knowledge and gathered data. As is illustrated by the numerical example, the proposed framework can well capture domain experts' diversity judgements and derive initial truth degrees for an FPN under different types of uncertainties.

Disassembly modeling and planning are meaningful and important to the reuse, recovery, and recycling of obsolete and discarded products. However, the existing methods pay little or no attention to resources constraints, e.g., disassembly operators and tools. Thus a resulting plan when being executed may be ineffective in actual product disassembly. This paper proposes to model and optimize selective disassembly sequences subject to multiresource constraints to maximize disassembly profit. Moreover, two scatter search algorithms with different combination operators, namely one with precedence preserved crossover combination operator and another with path-relink combination operator, are designed to solve the proposed model. Their validity is shown by comparing them with the optimization results from well-known optimization software CPLEX for different cases. The experimental results illustrate the effectiveness of the proposed method.

It is complex and difficult to perform the emergency scheduling of forest fires in order to reduce the operational cost and improve the efficiency of extinguishing fire services. A new research issue arises when: 1) decision-makers want to minimize the number of rescue vehicles (or fire-fighting ones) while minimizing the extinguishing time; and 2) decision-makers prefer to complete this task given limited vehicle resources. To do so, this paper presents a novel multiobjective scheduling model to handle forest fires subject to limited rescue vehicle (fire engine) constraints, in which a fire-spread speed model is introduced into this problem to better describe practical forestry fire. Moreover, a Multiobjective Hybrid Differential-Evolution Particle-Swarm-Optimization (MHDP) algorithm is proposed to create a set of Pareto solutions for this problem. This approach is applied to a real-world emergency scheduling problem of the forest fire in Mt. Daxing'anling, China. Its effectiveness is verified by comparing it with a genetic algorithm and particle swarm optimization algorithm. Experimental results show that the proposed approach is able to quickly produce satisfactory Pareto solutions.

Deciding an optimal location of a transportation facility and automotive service enterprise is an interesting and important issue in the area of facility location allocation (FLA). In practice, some factors, i.e., customer demands, allocations, and locations of customers and facilities, are changing, and thus, it features with uncertainty. To account for this uncertainty, some researchers have addressed the stochastic time and cost issues of FLA. A new FLA research issue arises when decision makers want to minimize the transportation time of customers and their transportation cost while ensuring customers to arrive at their desired destination within some specific time and cost. By taking the vehicle inspection station as a typical automotive service enterprise example, this paper presents a novel stochastic multiobjective optimization to address it. This work builds two practical stochastic multiobjective programs subject to stochastic demand, varying velocity, and regional constraints. A hybrid intelligent algorithm integrating stochastic simulation and multiobjective teaching-learning-based optimization algorithm is proposed to solve the proposed programs. This approach is applied to a real-world location problem of a vehicle inspection station in Fushun, China. The results show that this is able to produce satisfactory Pareto solutions for an actual vehicle inspection station location problem.

Project scheduling problem (PSP) is to determine the resource allocation schedule for the trade-off between the project cost and the completion time. In this paper, PSP in the environment with uncertainty and randomness simultaneously is considered. In detail, the concepts of uncertain variable and uncertain random variable are introduced. Based on some concepts and theorems of chance theory, an uncertain random project scheduling model is built. For some special case, the proposed uncertain random programming model is transformed to a crisp mathematical programming model. Besides, uncertain random simulation techniques and genetic algorithm are integrated into a hybrid intelligent algorithm for searching the quasi-optimal schedule.

Transportation facility or automotive service enterprise location is an interesting and important issue. To improve transportation efficiency, many researchers have addressed the traditional facility location allocation (FLA) problem, e.g., the FLA problem with the minimum transportation cost or the maximum obtained profit. However, with the improvement of saving energy awareness and the enhancement of environmental concerns, energy efficient and low carbon emission should be considered as key factors influencing the FLA problem. To handle this issue via a more practical method, this work proposes a sustainable location issue for an automotive service enterprise. That is, by taking the vehicle inspection station as a typical automotive service enterprise and an example, this work presents new energy-efficient models of its sustainable location with carbon constraints. An artificial fish swarm algorithm is proposed to solve the proposed models. Some numerical examples are given to illustrate the proposed models and testify the effectiveness of the algorithm.

Facility location allocation (FLA) is considered as the problem of finding optimally a facility's location with the maximum customer satisfaction, the maximum profit of investors of the facility, and the minimum transportation cost of its oriented-customers. In practice, some factors of the FLA problem, i.e., customer demands, allocations, even locations of customers and facilities, are usually changing, and thus the problem features with uncertainty. To account for this uncertainty, some researchers have addressed the stochastic profit and cost issues of FLA. However, a decision-maker hopes to obtain the specific profit of investors of building facility and meanwhile to minimize the cost of target customers. To handle this issue via a more practical manner, it is essential to address the cost-profit tradeoff issue of FLA. Moreover, some region constraints can greatly influence FLA. By taking the vehicle inspection station as a typical automotive service enterprise example, this work presents new stochastic cost-profit tradeoff FLA models with region constraints. A hybrid algorithm integrating stochastic simulation and Genetic Algorithms (GA) is proposed to solve the proposed models. Some numerical examples are given to illustrate the proposed models and the effectiveness of the proposed algorithm.

Determining the glycan topology automatically from mass spectra represents a great challenge. Existing methods fall into approximate and exact ones. The former including greedy and heuristic ones can reduce the computational complexity, but suffer from information lost in the procedure of glycan interpretation. The latter including dynamic programming and exhaustive enumeration are much slower than the former. In the past years, nearly all emerging methods adopted a tree structure to represent a glycan. They share such problems as repetitive peak counting in reconstructing a candidate structure. Besides, tree-based glycan representation methods often have to give different computational formulas for binary and ternary glycans. We propose a new directed acyclic graph structure for glycan representation. Based on it, this work develops a de novo algorithm to accurately reconstruct the tree structure iteratively from mass spectra with logical constraints and some known biosynthesis rules, by a single computational formula. The experiments on multiple complex glycans extracted from human serum show that the proposed algorithm can achieve higher accuracy to determine a glycan topology than prior methods without increasing computational burden.

Facility location allocation (FLA) is one of the important issues in the logistics and transportation fields. In practice, since customer demands, allocations, and even locations of customers and facilities are usually changing, the FLA problem features uncertainty. To account for this uncertainty, some researchers have addressed the fuzzy profit and cost issues of FLA. However, a decision-maker needs to reach a specific profit, minimizing the cost to target customers. To handle this issue it is essential to propose an effective fuzzy cost-profit tradeoff approach of FLA. Moreover, some regional constraints can greatly influence FLA. By taking a vehicle inspection station as a typical automotive service enterprise example, and combined with the credibility measure of fuzzy set theory, this work presents new fuzzy cost-profit tradeoff FLA models with regional constraints. A hybrid algorithm integrating fuzzy simulation and genetic algorithms (GA) is proposed to solve the proposed models. Some numerical examples are given to illustrate the proposed models and the effectiveness of the proposed algorithm.

Disassembly planning is aimed to perform the optimal disassembly sequence given a used or obsolete product in terms of cost and environmental impact. However, the actual disassembly process of products can experience great uncertainty due to a variety of unpredictable factors. To deal with such uncertainty, this work presents some chance constrained programming models for disassembly cost from the perspective of stochastic planning. Moreover, two hybrid intelligent algorithms, namely, one integrating stochastic simulation and neural network (NN), and another integrating stochastic simulation, genetic algorithm (GA) and neural network (NN), are proposed to solve the proposed models, respectively. Some numerical examples are given to illustrate the proposed models and the effectiveness of proposed algorithms.

Disassembly is a systematic method to separate an end-of-life product into its constituent parts and components. However, the disassembly process of products can experience great uncertainty due to a variety of unpredictable factors. To deal with such uncertainty, this work proposes a novel probability analysis method of disassembly cost with random removal time and different removal labor cost. According to different constraints and actual execution of disassembly, it presents typical probability evaluation models of disassembly cost. Moreover, a solution algorithm based on stochastic simulation is used to solve the proposed probability models. Some numerical examples are given to illustrate the proposed concepts and the effectiveness of the proposed algorithm.